Systems and methods for assessing whether medical procedures should be approved

ABSTRACT

A rules engine that allows constructing and processing rules within a software application using protocol driven processing. A machine learning algorithm may be used to assess requested procedures that are initially rejected. The machine learning algorithm may also be saved periodically or at the time of each assessment and accessed at a later time.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/687,906, filed Jun. 21, 2018, the entire contents ofwhich are incorporated herein by reference.

BACKGROUND OF THE INVENTION

In 2016, U.S. health care costs were over $3 trillion. That amounted toapproximately 18 percent of gross domestic product. In comparison, in1960, health care cost were approximately $27 billion, just 5 percent ofGDP.

The price of medical care is the largest factor driving U.S. healthcarecosts, accounting for approximately 90% of spending. These expendituresinclude the cost of caring for those with chronic or long-term medicalconditions, an aging population, and the increased cost of newmedicines, procedures and technologies. Also, recent healthcare reformlaw has expanded access to insurance to millions of Americans. The U.S.has transitioned to a healthcare system in which everyone can obtainhealth insurance regardless of age or health status, and manyindividuals who are newly insured need ongoing medical attention.

To prevent health insurance costs from spiraling out of control,insurance companies have instituted various cost-containment procedures.Common cost-containment procedures, under which coverage may berestricted, include: pre-admission testing (additional testing performedto determine whether hospitalization or surgery is required innon-emergency situations); second opinions (a second opinion from ahealth care practitioner may be required before treatment isauthorized); prior consent for hospitalization (an insurance companymust pre-authorize a proposed treatment); and limitation of health careprocedures, services and related expenditures outside the usual,customary and reasonable charges for particular courses of care.However, most of these cost-containment procedures can delay treatmentand increase the time expended by medical professional on each patient.

Software solutions may be implemented to reduce the time needed forsubmission, review, and approval of procedure requests. Providers maysubmit patient clinical criteria and/or procedure requests via asoftware solution. If the information provided about a proposedprocedure or test meets evidence-based criteria, the software mayautomatically return an indication that the procedure or test isapproved. Providers may also be presented with alternatives to theoriginally requested test or procedure.

If the treatment falls outside of pre-set clinical guidelines, or if theinitial request is unable to be resolved, the request may be flaggedand/or sent for review by, for example, a nurse practitioner. During thereview, a nurse practitioner may evaluate the request and provideapprovals for treatments within clinical guidelines. For procedures thatfall outside of these criteria, the nurse practitioner may discuss analternative care pathway. If consensus is not reached, the request maybe further escalated for a peer-to-peer consultation. During apeer-to-peer consultation, a physician may review the treatment. Afterconsultation with the requesting provider, the procedure may beapproved, or the provider may agree to perform a different procedure orno procedure. If agreement is not reached with the provider, coveragemay be denied.

However, initial review and subsequent peer-to-peer consultation—i.e.,the “off-ramping” decision process that may initiate from the softwaresolution but ultimately requires further consideration—can be timeconsuming. Time and resources devoted to those reviews could be moreefficiently used to treat patients.

SUMMARY OF THE INVENTION

The present invention is directed to improved systems and methods foroff-ramping automated decision processing engines. A machine learningalgorithm may be used to review and approve requested procedures thatare initially flagged for denial by the processing engine, but which aresimilar to procedures that have been approved historically.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the invention can be obtained by reference toembodiments set forth in the illustrations of the accompanying drawings.Although the illustrated embodiments are merely exemplary of systems,methods, and apparatuses for carrying out the invention, both theorganization and method of operation of the invention, in general,together with further objectives and advantages thereof, may be moreeasily understood by reference to the drawings and the followingdescription. Like reference numbers generally refer to like features(e.g., functionally similar and/or structurally similar elements).

The drawings are not necessarily depicted to scale; in some instances,various aspects of the subject matter disclosed herein may be shownexaggerated or enlarged in the drawings to facilitate an understandingof different features. Also, the drawings are not intended to limit thescope of this invention, which is set forth with particularity in theclaims as appended hereto or as subsequently amended, but merely toclarify and exemplify the invention.

FIG. 1 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 2 depicts an exemplary database map in accordance with the presentinvention;

FIG. 3 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 4 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 5 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 6 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 7 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 8 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 9 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 10 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 11 depicts an exemplary tree view of a protocol;

FIG. 12 depicts an exemplary user interface in accordance with thepresent invention;

FIG. 13 depicts an exemplary user interface in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention may be understood by reference to the following detaileddescriptions of embodiments of the invention. However, techniques,systems, and operating structures in accordance with the invention maybe embodied in a wide variety of forms and modes, some of which may bequite different from those in the disclosed embodiments. Also, thefeatures and elements disclosed herein may be combined to form variouscombinations without exclusivity, unless expressly stated otherwise.Consequently, the specific structural and functional details disclosedherein are merely representative. Yet, in that regard, they are deemedto afford the best embodiments for purposes of disclosure and to providea basis for the claims herein, which define the scope of the invention.It must be noted that, as used in the specification and the appendedclaims, the singular forms “a”, “an”, and “the” include plural referentsunless the context clearly indicates otherwise.

The present invention is directed to an improved medical protocol drivenprocedure assessment engine. The new assessment engine may be designedto take into account (1) pass rate requirements for rules; (2) weightedaverages of criteria within a rule; and/or (3) iterative rule processing(i.e., branching). Rules may be built based on medical protocolsconfigured based on diagnosis (DX) codes. Each DX may have a set ofrules pre-configured.

Protocols and Decision Nodes

Each protocol may be established with multiple decision nodes, and oneor more DX codes may be selected for each protocol. Each decision nodemay consist of clinical conditions and clinical criteria. The userinterface shown in FIG. 1 is an example of an interface that may be usedto create a new DX protocol. The user may enter a name or descriptionfor the Protocol. The user may then select DX codes. DX codes may besearched by code or code range, or by using type ahead in asearch-by-name box.

The user may add decision nodes (decision branches) to the protocol.Each decision node may consist of clinical conditions, rules, clinicalcriteria, procedure codes/scores, and/or triage. Once a decision nodehas been added, it may be edited, moved, or removed. The elements of theprocedure assessment engine (e.g., protocols, decision nodes, clinicalconditions, rules, clinical criteria) may be established and saved as aseries of database tables. FIG. 2 shows an example databasesmap—including exemplary databases and database fields—that may be usedto generate and practice a procedure assessment engine in accordancewith the present invention.

Clinical Conditions

The user interface shown in FIG. 3 is an example interface that may beused to add clinical conditions to a decision node. The user may bepresented, for example, with a drop-down list of existing clinicalconditions. In the alternative, the user may type the name of a clinicalcondition and the interface may display a type-ahead list from which aclinical condition may be selected. In the alternative, the user maysimply type the name of a new clinical condition. In a preferredembodiment, the condition name is unique to the decision node. If theuser types in a condition already found in the decision node, an errormessage may be displayed.

Rules

The user interfaces shown in FIGS. 4 and 5 are example interfaces thatmay be used to add rules to clinical conditions. In FIG. 4, the user mayselect “Add Rule” to add a rule. The system may then display theinterface shown in FIG. 5, which may be used to enter information aboutthe rule. When adding a new rule, the user may enter (or be required toenter) one or more of the following: (1) an action (pass or fail), (2)an indication to be associated with the requested procedure or a queueto which the case may be escalated if the rule is failed, (3) a nextprotocol, (4) a next node, and/or (5) a confidence threshold number. Theuser may also be able to enter an order number which would be used todetermine the order in which the rules are processed. When the procedureassessment engine is run, it may present the rules in order (e.g.ascending or descending) according to the order numbers assigned to eachrule.

In a preferred embodiment, every rule is assigned an “action.” The“action” determines whether the rule is a “Pass Rule” or “Fail Rule.”The user interface may present to the user a drop-down menu with theoption of “Pass” or “Fail.” When the procedure assessment engine is run,if a Pass Rule, is met the procedure assessment engine may approve therequested procedure. In addition or in the alternative, the user may bepresented with a list of approved procedures, one of which may be theprocedure originally proposed by the user. If a Pass Rule is not met,the procedure assessment engine may proceed to the next rule. If a FailRule is met, the procedure assessment engine may deny the requestedprocedure. In a preferred embodiment, a procedure may be approved if atleast one Pass Rule is met, and no Fail Rules are met.

FIG. 6 depicts an example user interface showing two exemplary rules,Rules 215881 and 215882. In a preferred embodiment, each rule isassigned a unique ID. Rule 215881 is an example of a Fail Rule and Rule215882 is an example of a Pass Rule. Fail Rules may specify a queue towhich a requested procedure may be assigned if the Fail Rule is met. Asshown in FIG. 6, if Rule 215881 is failed, the case would be assigned toa Clinical Coordinator.

Also as shown in FIG. 6, each rule may have a confidence value. Theconfidence threshold number may be any number and determines the totalof the clinical criteria values that must be met in order for the ruleto be met (pass or fail). Each clinical criteria associated with a ruleis given a value and the total of those values must be greater than theconfidence threshold number for the rule to be met.

The “next protocol” and “next node” fields determine whether the userwill have to proceed to another protocol or another node if a Pass Ruleis met.

As shown in FIG. 7, once a rule has been added, a field for DX codes maybe displayed. This field may allow the user to select one or more DXcodes that would apply to the rule. The user may be presented with adrop-down menu that may display the full DX code list from the protocol.The list of DX codes may be displayed with checkboxes that allow one ormore codes to be selected.

Clinical Criteria

A set of clinical criteria may be associated with each rule. Referringto FIGS. 8 and 9, to add new clinical criteria to a rule, the user canstart typing in the criteria box and a type-ahead list will bedisplayed. The user can select an existing criteria from the list, orenter new criteria. Each criteria has a criteria type. The availablecriteria types may be displayed in a drop-down menu as shown, forexample, in FIG. 10. Example criteria types are age, gender, weight,height, and body mass index (BMI). Certain criteria such as age, gender,weight, and height may be stored in the system or in another systemwhich the procedure assessment engine may access. When the procedureassessment engine is run, it may retrieve such criteria. Certaincriteria may be calculated based on other criteria values. For example,BMI may be determined from a patient's weight and height.

As described above, each clinical criteria may be assigned a confidencevalue. A value entered for a criteria may indicate to the system thatthe criteria should be shown for the clinical condition in the userinterface when the procedure assessment engine in run. For each criteriaselected, the confidence value of the criteria are added together. Ifthe sum of the confidence values meets or exceeds the confidencethreshold number assigned to a rule, then the rule is met (e.g., passedor failed). The confidence threshold number may be restricted to between1 and 10, 1 and 100, or any other range. Dynamic criteria may also beused, for example: $male$ or $age>50$.

For the system to determine if the requested procedure is the bestprocedure, the rules may also consist of procedure codes and scores.This information may be used to show the user the results page and offeralternative procedures based on the score.

As shown in FIG. 11, each protocol may be viewed as a tree view. Thetree view may show the protocol, decision nodes, and clinicalconditions.

Although a procedure assessment engine is described herein as used toassess proposed medical procedures, the assessment engine may also beused in other contexts. Referring to FIG. 12, a user interface is shownfor a procedure assessment engine that may be used for aircraft repair.Two conditions are shown: (1) “Vibration originates from tail”; and (2)“Vibration originates from head.” Three criteria are associated witheach condition, and the rules for each condition may require all threecriteria to be met. The criteria “Vibration present during Autorotation”is associated with both conditions. Since the criteria “Vibrationpresent during Autorotation” is selected in the first condition(Vibration originates from tail), the same criteria may also bedetermined as met for the second condition “Vibration originates fromhead,” even if it is not selected for the second condition.

Analyzing Rules and Results

A variable that may be taken into consideration when processing therules and presenting the results may be the user status. For example, ifthe user of the assessment engine is identified as a physician, theresults of the assessment may be presented as a score and the user maybe offered choices to change the requested procedure. As anotherexample, if the user of the assessment engine is identified as a clerk,the assessment engine may indicate whether the requested procedure isapproved and may present alternatives in simple terms. A clerk may alsobe presented with a message based on a CPT/DX.

When the procedure assessment engine is run, after the rules have beenanalyzed, the user may be presented with a results screen. An example ofa results screen is shown in FIG. 13. The user may be provided with alist of alternative procedures. The list may be sorted according toscore (e.g., descending) or cost (e.g., ascending). The score and/orcost may be shown on the results screen.

If the original requested procedure is denied but one or morealternative procedures are presented to the user, the user may select analternative procedure. The alternative procedure may then be substitutedfor the original requested procedure and approved immediately.Alternatively, the user may provide an input to the assessment engineindicating that the user is withdrawing the request. Alternatively, theuser may provide an input to the system indicating that the user choosesto maintain the original request.

If the user maintains the original request, the request may be forwardedto a review queue for review by, for example, a nurse practitioner orfor peer-to-peer consultation. In the alternative, the request may beevaluated using a machine learning algorithm. The machine learningalgorithm may be a neural network. The machine learning algorithm mayoutput a score representing the probability that the request procedureis necessary. The score can be compared to a threshold score todetermine whether the procedure should be approved. An indication ofwhether the procedure is approved may be displayed as described above.As described above, if a procedure is not approved, alternativeprocedures may be suggested.

The data input to the machine learning algorithm may include (1)clinical criteria; (2) medical data for a patient; (3) one or morediagnosis codes; and/or (4) information concerning whether the sameprocedure requested for patients with similar medical data and/orsimilar clinical criteria was approved. The medical data may include,for example, the patient's age, gender, weight, and/or height, thenumber of times the procedure has been previously administered to thepatient, and the amount of time that has passed since the requestedprocedure was last administered to the patient. Other data input to themachine learning algorithm may include data indicating the percentage oftimes that the procedure has been approved when forwarded to aparticular review queue.

The output from the machine learning algorithm may be monitored and thesystem may further receive data concerning whether each output scoreshould have been higher or lower. As additional requests and data areassessed by the machine learning algorithm, the algorithm may changeover time. For diagnostic purposes, or in case a decision whether toapprove a procedure is challenged, a system in accordance with thepresent invention may periodically save the machine learning algorithm.For example, the machine learning algorithm may be saved every day. Inaddition or in the alternative, the machine learning algorithm may besaved immediately before or immediately after every assessment. Themachine learning algorithm may be saved on computer storage medium sothat the state of the machine learning algorithm at any point in timemay be retrieved at a later time, after further assessments areperformed. In other words, the system may perform the following steps:(1) save the machine learning algorithm in a first state; (2) receive aselection of a second set of one or more clinical criteria; (3)determine whether the sum of the confidence values associated with thesecond set of one or more clinical criteria exceed a confidencethreshold number; (4) in response to the determination of whether thesum of the confidence values associated with the second set of one ormore clinical criteria exceed the confidence threshold number, use themachine learning algorithm to generate an output indicating whether asecond medical procedure should be approved, wherein the machinelearning algorithm changes from the first state to a second state; and(5) after generating the output indicating whether the second medicalprocedure should be approved, retrieving the machine learning algorithmin the first state.

In addition or in the alternative, a system or method in accordance withthe present invention may include an algorithm that may be used todetermine whether a rule is blocked. A first rule may be blocked if itis associated with the same criteria as a second rule plus one or moreadditional criteria that are not associated with the second rule. Inthat instance, the first rule may never be considered. A rule also maybe blocked if two associated criteria are contradictory.

The present invention includes a computer readable medium having programcode recorded thereon, for execution on a computer having a graphicaluser interface and a user input device, said program code configured togenerate or modify a protocol hierarchy according to a methodcomprising: receiving a request to create a decision node of theprotocol hierarchy; entering in a first database table a first recordrepresenting the requested decision node; receiving a request toassociate a condition with the decision node; entering in a seconddatabase table a second record representing the condition and anassociation between the condition and the decision node; receiving arequest to associate a plurality of rules with the condition; enteringin a third database table a plurality of rule records, wherein each rulerecord represents a rule and an association between the rule and thecondition; receiving a request to associate a plurality of criteria witha rule; entering in a fourth database table a plurality of criteriarecords, wherein each criteria record represents a criterion and anassociation between the criterion and a rule; upon entering a firstcriteria record in the fourth database table, determining whether therule associated with the criterion in the first criteria record isblocked; upon determining that a rule is blocked, presenting on thegraphical user interface a first graphical element representing theoption to delete a criteria record, and a second graphical elementrepresenting the option to edit a criteria record; receiving an inputcorresponding to selection of one of the first graphical element or thesecond graphical element; wherein if an input corresponding to selectionof the second graphical element is received, presenting on the graphicaluser interface data from the first criteria record.

The present invention further includes systems and methods for assessingwhether one or more medical procedures should be approved, such as acomputer memory having a machine-readable medium comprisingmachine-executable code recorded thereon, said machine-executable codecomprising instructions for: (1) entering in a first database table afirst record wherein said first record represents a first rule andwherein said first record comprises a confidence threshold number; (2)entering in a second database table one or more records wherein eachrecord represents a clinical criterion and wherein each record comprisesa confidence value associated with the clinical criterion; (3) receivinga selection of a first set of one or more clinical criteria; (4)determining whether the sum of the confidence values associated with thefirst set of one or more clinical criteria exceed the confidencethreshold number; (5) in response to the determination of whether thesum of the confidence values associated with the first set of one ormore clinical criteria exceed the confidence threshold number, receivingmedical data and processing the medical data and the first set of one ormore clinical criteria using a first machine learning algorithm togenerate an output indicating whether the medical procedure should beapproved; (6) displaying on a computer screen an indication of whetherthe procedure is approved.

While the invention has been described in detail with reference toembodiments for the purposes of making a complete disclosure of theinvention, such embodiments are merely exemplary and are not intended tobe limiting or represent an exhaustive enumeration of all aspects of theinvention. It will be apparent to those of ordinary skill in the artthat numerous changes may be made in such details, and the invention iscapable of being embodied in other forms, without departing from thespirit, essential characteristics, and principles of the invention.Also, the benefits, advantages, solutions to problems, and any elementsthat may allow or facilitate any benefit, advantage, or solution are notto be construed as critical, required, or essential to the invention.The scope of the invention is to be limited only by the appended claims.

What is claimed is:
 1. A system for assessing whether one or moremedical procedures should be approved, comprising: a computer memoryhaving a non-transitory machine-readable medium comprisingmachine-executable code recorded thereon, said machine-executable codecomprising instructions for: entering in a first database table a firstrecord wherein said first record represents a first rule and whereinsaid first record comprises a confidence threshold number; entering in asecond database table one or more records wherein each record representsa clinical criterion and wherein each record comprises a confidencevalue associated with the clinical criterion; receiving a selection of afirst set of one or more clinical criteria; determining whether the sumof the confidence values associated with the first set of one or moreclinical criteria exceed the confidence threshold number; in response tothe determination of whether the sum of the confidence values associatedwith the first set of one or more clinical criteria exceed theconfidence threshold number, receiving medical data and processing themedical data and the first set of one or more clinical criteria using afirst machine learning algorithm to generate a first output indicatingwhether a first medical procedure should be approved; after generatingthe first output, saving a first state of the machine learningalgorithm; displaying on a computer screen an indication of whether theprocedure is approved; receiving a selection of a second set of one ormore clinical criteria; determining whether the sum of the confidencevalues associated with the second set of one or more clinical criteriaexceed the confidence threshold number; in response to the determinationof whether the sum of the confidence values associated with the secondset of one or more clinical criteria exceed the confidence thresholdnumber, using the machine learning algorithm to generate a second outputindicating whether a second medical procedure should be approved,wherein the machine learning algorithm is converted from the first stateto a second state; after generating the second output indicating whetherthe second medical procedure should be approved, saving the second stateof the machine learning algorithm and retrieving the first state of themachine learning algorithm; and after retrieving the first state of themachine learning algorithm, receiving a selection of a third set of oneor more clinical criteria, determining whether the sum of the confidencevalues associated with the clinical criteria exceed the confidencethreshold number, and using the machine learning algorithm in the secondstate to generate a third output indicating whether a second medicalprocedure should be approved.
 2. The system of claim 1, wherein themachine-executable code further comprises instructions for displaying onthe computer screen one or more alternative procedures.
 3. The system ofclaim 2, wherein the medical data comprises one or more selected fromthe group consisting of a patient's age, a patient's gender, a patient'sweight, a patient's height, and the number of times the procedure hasbeen previously administered.
 4. The system of claim 2, wherein themachine learning algorithm is a neural network.
 5. The system of claim4, wherein the medical data comprises one or more selected from thegroup consisting of a patient's age, a patient's gender, a patient'sweight, a patient's height, and the number of times the procedure hasbeen previously administered.
 6. The system of claim 1, wherein themedical data comprises one or more selected from the group consisting ofa patient's age, a patient's gender, a patient's weight, a patient'sheight, and the number of times the procedure has been previouslyadministered.
 7. The system of claim 6, wherein the machine learningalgorithm is a neural network.
 8. The system of claim 1, wherein themachine learning algorithm is a neural network.
 9. A method forassessing whether one or more medical procedures should be approved,comprising: entering in a first database table a first record whereinsaid first record represents a first rule and wherein said first recordcomprises a confidence threshold number; entering in a second databasetable one or more records wherein each record represents a clinicalcriterion and wherein each record comprises a confidence valueassociated with the clinical criterion; receiving a selection of a firstset of one or more clinical criteria; determining whether the sum of theconfidence values associated with the first set of one or more clinicalcriteria exceed the confidence threshold number; in response to thedetermination of whether the sum of the confidence values associatedwith the first set of one or more clinical criteria exceed theconfidence threshold number, receiving medical data and processing themedical data and the first set of one or more clinical criteria using afirst machine learning algorithm to generate a first output indicatingwhether a first medical procedure should be approved; after generatingthe first output, saving a first state of the machine learningalgorithm; displaying on a computer screen an indication of whether theprocedure is approved; receiving a selection of a second set of one ormore clinical criteria; determining whether the sum of the confidencevalues associated with the second set of one or more clinical criteriaexceed the confidence threshold number; in response to the determinationof whether the sum of the confidence values associated with the secondset of one or more clinical criteria exceed the confidence thresholdnumber, using the machine learning algorithm to generate a second outputindicating whether a second medical procedure should be approved,wherein the machine learning algorithm is converted from the first stateto a second state; after generating the second output indicating whetherthe second medical procedure should be approved, saving the second stateof the machine learning algorithm and retrieving the first state of themachine learning algorithm; and after retrieving the first state of themachine learning algorithm, receiving a selection of a third set of oneor more clinical criteria, determining whether the sum of the confidencevalues associated with the clinical criteria exceed the confidencethreshold number, and using the machine learning algorithm in the secondstate to generate a third output indicating whether a second medicalprocedure should be approved.
 10. The method of claim 9, furthercomprising displaying on the computer screen one or more alternativeprocedures.
 11. The method of claim 10, wherein the medical datacomprises one or more selected from the group consisting of a patient'sage, a patient's gender, a patient's weight, a patient's height, and thenumber of times the procedure has been previously administered.
 12. Themethod of claim 10, wherein the machine learning algorithm is a neuralnetwork.
 13. The method of claim 12, wherein the medical data comprisesone or more selected from the group consisting of a patient's age, apatient's gender, a patient's weight, a patient's height, and the numberof times the procedure has been previously administered.
 14. The methodof claim 9, wherein the medical data comprises one or more selected fromthe group consisting of a patient's age, a patient's gender, a patient'sweight, a patient's height, and the number of times the procedure hasbeen previously administered.
 15. The method of claim 14, wherein themachine learning algorithm is a neural network.
 16. The method of claim9, wherein the machine learning algorithm is a neural network.
 17. Asystem for assessing whether one or more medical procedures should beapproved, comprising: a computer memory having a non-transitorymachine-readable medium comprising machine-executable code recordedthereon, said machine-executable code comprising instructions for:entering in a first database table a first record wherein said firstrecord represents a first rule and wherein said first record comprises aconfidence threshold number; entering in a second database table one ormore records wherein each record represents a clinical criterion andwherein each record comprises a confidence value associated with theclinical criterion; receiving a selection of a first set of one or moreclinical criteria; determining whether the sum of the confidence valuesassociated with the first set of one or more clinical criteria exceedthe confidence threshold number; in response to the determination ofwhether the sum of the confidence values associated with the first setof one or more clinical criteria exceed the confidence threshold number,receiving medical data and processing the medical data and the first setof one or more clinical criteria using a first machine learningalgorithm to generate a first output indicating whether a first medicalprocedure should be approved; after generating the first output, savinga first state of the machine learning algorithm; displaying on acomputer screen an indication of whether the procedure is approved;receiving a selection of a second set of one or more clinical criteria;determining whether the sum of the confidence values associated with thesecond set of one or more clinical criteria exceed the confidencethreshold number; in response to the determination of whether the sum ofthe confidence values associated with the second set of one or moreclinical criteria exceed the confidence threshold number, using themachine learning algorithm to generate a second output indicatingwhether a second medical procedure should be approved, wherein themachine learning algorithm is converted from the first state to a secondstate; after generating the second output, saving the second state ofthe machine learning algorithm and retrieving the first state of themachine learning algorithm; receiving a selection of a third set of oneor more clinical criteria; determining whether the sum of the confidencevalues associated with the third set of one or more clinical criteriaexceed the confidence threshold number; in response to the determinationof whether the sum of the confidence values associated with the thirdset of one or more clinical criteria exceed the confidence thresholdnumber, using the machine learning algorithm to generate a third outputindicating whether a third medical procedure should be approved, whereinthe machine learning algorithm is converted from the second state to athird state; after generating the third output, retrieving the firststate of the machine learning algorithm.
 18. The system of claim 17,wherein the machine-executable code further comprises instructions fordisplaying on the computer screen one or more alternative procedures.19. The system of claim 17, wherein the medical data comprises one ormore selected from the group consisting of a patient's age, a patient'sgender, a patient's weight, a patient's height, and the number of timesthe procedure has been previously administered.
 20. The system of claim17, wherein the machine learning algorithm is a neural network.